4 Mitacs Globalink (GRI) research projects for Summer 2027.
1. AI Guided Defect Tolerance Maps for Energy and Extreme Environment Materials
Materials used in energy and extreme environments must operate under conditions that push them far from equilibrium, including high temperature, electrochemical polarization, radiation exposure, chemical gradients, and mechanical stress. Under these conditions, defects such as vacancies, interstitials, dopants, trapped ions, and defect clusters can form, migrate, and interact. These atomic-scale processes often determine whether a material remains stable or degrades during operation.
This project invites a motivated undergraduate intern to build AI guided defect tolerance maps for functional materials used in energy and extreme environment applications. The project will focus on selected material families such as functional oxides, proton conducting ceramics, battery materials, and radiation tolerant ceramics. The intern will collect and organize information from literature and group datasets, including material composition, crystal structure, defect type, operating condition, reported stability, transport behavior, and degradation mechanism.
The project is structured in two phases. In the first phase, the intern will learn the basic concepts of defect formation, defect migration, and material degradation under nonequilibrium conditions. They will then build a structured dataset from selected literature and research data. In the second phase, the intern will use data visualization, descriptor analysis, and simple machine learning assisted methods to identify trends that connect material chemistry and structure to defect tolerance.
By the end of the internship, the student will produce a curated dataset, visual defect tolerance maps, summary figures, a short technical report, and a final group presentation. The project is ideal for students interested in materials science, energy technologies, nuclear materials, data science, scientific visualization, or graduate research in computational and data driven materials design.
Research area, student roles & skills
Research area: Dr. Meng Li is an Assistant Professor in Mechanical and Materials Engineering at Queen’s University and a former Senior Staff Scientist at Idaho National Laboratory in the U.S. Her research combines computation, machine learning, and experiment to understand how atoms, ions, and defects control the performance of advanced energy materials. She has authored 61 peer-reviewed publications, including 25 as lead author, with more than 3,700 citations, and her work has appeared in Nature Energy, Nature Catalysis, Chem, ACS Energy Letters, Nano Energy, and Materials Today. She has contributed to 6 U.S. patents and has led many projects.
Student roles:
The student will play an active role in a data driven materials research project focused on defect tolerance in energy and extreme environment materials. Working closely with the supervisor and research group, the student will:
• Learn the basic concepts of defect chemistry, defect formation, defect migration, ion transport, radiation damage, and materials degradation under nonequilibrium conditions.
• Collect and organize data from selected literature and group datasets, including material composition, crystal structure, defect type, operating condition, transport behavior, stability, and degradation mechanism.
• Build a structured database using Excel, Python, or similar tools.
• Develop simple materials descriptors such as composition, ionic radius, lattice parameter, defect type, bonding environment, operating temperature, atmosphere, or irradiation condition.
• Generate visual defect tolerance maps that compare how different materials respond to electrochemical, thermal, chemical, or radiation-related driving forces.
• Use simple statistical or machine learning assisted methods to identify trends between materials descriptors and defect-related performance.
• Prepare figures, visualizations, and short written summaries that connect data trends to physical mechanisms.
• Present progress in regular group meetings and receive feedback from the supervisor and other group members.
The intern will begin with guided reading and example datasets before moving to a focused materials family. The emphasis will be on learning how to convert scattered materials information into physically meaningful design principles for materials operating under demanding conditions.
The student’s work will be integrated into an active research project, with the goal of contributing to a future research manuscript. Students who complete the planned analysis and contribute to the interpretation and preparation of the results will be included as coauthors.
By the end of the internship, the student will have gained practical experience in defect chemistry, materials data curation, Python-based analysis, scientific visualization, literature synthesis, and research communication.
Skills required:
We are looking for a curious and motivated undergraduate student with a background in materials science, chemistry, physics, engineering, nuclear engineering, computer science, data science, or a related field. Basic experience with Python, Excel, or other data analysis tools is helpful. Prior experience with defect physics, radiation effects, electrochemistry, or machine learning is not required. A solid understanding of general chemistry, crystal structures, or materials properties will be useful. This project is well suited for students who enjoy data analysis, literature synthesis, visualization, and connecting materials behavior to atomic-scale mechanisms.
2. Computational Design of Sustainable Materials for Energy and Environmental Applications
This internship program offers interns the opportunity to contribute to cutting-edge research in computational materials science, with a focus on sustainable solutions for energy and environmental challenges. Over three months, the interns will support ongoing research projects led by the Insilico Matters Laboratory (IML) at INRS. These projects aim to accelerate the discovery and design of affordable, efficient, and scalable materials for applications such as clean energy production, storage, and conversion.
Interns will engage in tasks related to the generation and organization of high-quality materials data using advanced computational techniques. These techniques may include density functional theory (DFT), molecular dynamics (MD), and other multi-scale simulation methods. A strong emphasis will also be placed on data management and preparation to support future integration with machine learning models.
While the specific project each intern will undertake will be finalized based on current research needs and the interns’ backgrounds, all projects will align with the lab’s overarching mission of designing materials for next-generation green technologies.
Interns will have the opportunity to:
1. Gain hands-on experience with advanced computational tools used in materials science (e.g., DFT, MD, Virtual NanoLab).
2. Learn to use national high-performance computing infrastructure (e.g., through the Digital Research Alliance of Canada: https://alliancecan.ca/en).
3. Explore how computational approaches can help solve real-world problems in the green energy and sustainability sectors.
4. Collaborate with a diverse and interdisciplinary team of researchers working on projects such as fuel cells, catalytic materials, and sustainable fuel production.
5. Contribute to the broader mission of the Computational Energy Materials Design Infrastructure (CEMDI) (www.cemdi.inrs.ca), a platform fostering innovation through collaborative computational research.
6. This internship will provide a unique opportunity for students to build valuable skills in scientific computing, materials modeling, and data science, while contributing to the global transition toward a more sustainable energy future.
Research area, student roles & skills
Research area: Computational materials design for energy and environmental applications, Sustainable technologies, materials and chemical processes, Photo- and Electro-catalysis, Solid oxide fuel cells, Surfaces and interfaces, Disordered Materials, Nanomaterials, Point defects, Grain Boundaries, Quantum and Classical Mechanics Simulations, Machine learning, Density Functional Theory, Molecular Dynamics
Student roles:
The student will design, optimize, and validate material models using advanced computational techniques. They will work closely with Ph.D. students to generate data on atomic- and electronic-scale properties. Key responsibilities include conducting literature reviews, presenting weekly progress in one-on-one meetings, contributing to research reports, delivering monthly updates to the full team, and supporting data representation and management. A proactive attitude, attention to detail, and strong communication skills are essential.
Skills required:
Students with a strong background in Physical Chemistry, Materials Science and Engineering, Chemistry, Physics, Computational Science, Data Science, Machine Learning, Mathematics, or related fields are encouraged to apply. Fluency in English (spoken and written) is required; proficiency in French is an asset. The ideal candidate should demonstrate the ability to work both independently and collaboratively in a research team. Strong critical thinking, problem-solving skills, and a willingness to learn are essential. Prior experience with computational tools or coding is helpful but not mandatory.
3. Data Driven Defect Chemistry Maps for Proton Conducting Oxides
Proton conducting oxides are key materials for next-generation hydrogen technologies, including protonic ceramic fuel cells, electrolyzers, hydrogen separation membranes, and related electrochemical devices. Their performance is controlled by defects: dopants create oxygen vacancies, oxygen vacancies enable hydration, and protons move through the crystal lattice by interacting with local atomic environments. Understanding these defect chemistry relationships is essential for designing materials that conduct ions efficiently while remaining stable under operating conditions.
This project invites a motivated undergraduate intern to build data driven defect chemistry maps for proton conducting oxides. The student will curate information from published literature and group datasets, including material composition, crystal structure, dopants, lattice parameters, hydration behavior, conductivity, defect formation energies, and reported operating conditions. Using Python or spreadsheet-based analysis, the student will organize these data into a structured database and visualize trends that connect composition, defect chemistry, and ion transport.
The project is structured in two phases. In the first phase, the intern will learn the fundamentals of proton conducting oxides and build a curated dataset from selected materials families. In the second phase, the intern will generate descriptor maps and simple statistical or machine learning assisted correlations to identify which materials features are most strongly associated with proton incorporation, transport, and stability.
By the end of the internship, the student will produce a structured dataset, analysis notebooks, summary figures, a short technical report, and a final group presentation. This project is ideal for students interested in energy materials, hydrogen technologies, data science, defect chemistry, or graduate research in materials science.
Research area, student roles & skills
Research area: Dr. Meng Li is an Assistant Professor in Mechanical and Materials Engineering at Queen’s University and a former Senior Staff Scientist at Idaho National Laboratory in the U.S. Her research combines computation, machine learning, and experiment to understand how atoms, ions, and defects control the performance of advanced energy materials. She has authored 61 peer-reviewed publications, including 25 as lead author, with more than 3,700 citations, and her work has appeared in Nature Energy, Nature Catalysis, Chem, ACS Energy Letters, Nano Energy, and Materials Today. She has contributed to 6 U.S. patents and has led many projects.
Student roles:
The student will play an active role in a data driven materials research project focused on defect chemistry in proton conducting oxides. Working closely with the supervisor and research group, the student will:
• Learn the basic concepts of proton conducting oxides, defect chemistry, hydration reactions, oxygen vacancies, dopants, and ion transport.
• Collect and organize data from selected literature and group datasets, including material composition, crystal structure, dopant chemistry, conductivity, hydration behavior, and operating conditions.
• Build a structured database using Excel, Python, or similar tools.
• Develop simple descriptors such as dopant size, charge, electronegativity, lattice parameter, tolerance factor, defect formation energy, or reported transport properties.
• Generate clear visual maps and plots to compare how composition, structure, and defect chemistry influence proton incorporation and ion transport.
• Prepare short written summaries that explain the physical meaning behind observed trends.
• Present progress in regular group meetings and receive feedback from the supervisor and other group members.
The intern will begin with guided reading and example datasets before moving to a focused materials family. The emphasis will be on learning how to convert scattered literature and computational data into physically meaningful materials design principles.
The student’s work will be integrated into an active research project, with the goal of contributing to a future research manuscript. Students who complete the planned analysis and contribute to the interpretation and preparation of the results will be included as coauthors.
By the end of the internship, the student will have gained practical experience in defect chemistry, materials data curation, Python-based analysis, scientific visualization, literature synthesis, and research communication. Final deliverables will include a curated dataset, analysis files or notebooks, summary figures, a short technical report, and a final presentation.
Skills required:
We are looking for a curious and motivated undergraduate student with a background in materials science, chemistry, chemical engineering, physics, engineering, data science, or a related field. Basic experience with Python, Excel, or other data analysis tools is helpful. Prior experience with defect chemistry, density functional theory, or machine learning is not required. A solid understanding of general chemistry, crystal structures, or materials properties will be useful. The project is well suited for students who enjoy literature analysis, data organization, visualization, and connecting materials data to physical mechanisms.
4. Developing low-cost ceramic membrane (CM) with natural geomaterial
This project explores low-cost, green, and functional materials for developing inorganic membranes used for water and wastewater treatment. The objective is to replace or reduce the consumption of fossil-based polymers and costly ceramics in membrane fabrication.
In specific, natural geomaterials will be sampled to create low-cost inorganic membranes. The sampled materials will be crushed using a ball mill and sieved to obtain micro- and nanoparticles. Then, the particles will be shaped using a compression molding machine under high pressure and sintered at controlled temperatures.
The membrane preparation will be optimized via controlling the parameters including the particle size, the geomaterials ratio, and sintering temperature and time. The mechanical strength, porosity, and pore size of the membranes will be evaluated. Two types of ceramic membrane, flat sheet and tubular, will be prepared through dry pressing and extrusion techniques, respectively.
The membranes with a relatively large and smaller pore size will be developed through controlling the particle diameters.
The prepared membrane will be used as a microfiltration or an ultrafiltration membrane to remove organic pollutant in water and wastewater treatment.
Research area, student roles & skills
Research area: Membrane Technology
Electrochemical Process
Water and Wastewater Treatment
Membrane Fouling and Scaling Control
Industrial Resource Recovery
Student roles:
Collaborating with the PhD or master students in the group for project design, literature
review, experimental conduction, report writing, and project presentation.
Attending the weekly individual meeting and group meeting, attending the group social
activities, and taking certain tasks in the lab (e.g., lab cleaning) as a formal member.
Skills required:
One of the following background:
(1) Environmental Engineering
(2) Chemical Engineering
(3) Chemistry Science
(4) Materials or Material Engineering
(5) Civil Engineering